Modelling the impact of sodium intake on cardiovascular disease mortality in Mexico

Background Cardiovascular diseases (CVD) represent the main cause of death in Mexico, while high blood pressure is suffered by about half of the adult population. Sodium intake is one of the main risk factors for these diseases. The Mexican adult population consumes about 3.1 g/day, an amount that exceeds what is recommended by the World Health Organization (WHO) < 2 g sodium/day. The objective of this study was to estimate the impact of reducing sodium intake on CVD mortality in Mexico using a scenario simulation model. Methods The Integrated Model of Preventable Risk (PRIME) was used to estimate the number of deaths prevented or postponed (DPP) due to CVD in the Mexican adult population following the following sodium intake reduction scenarios: (a) according to the WHO recommendations; (b) an “optimistic” reduction of 30%; and (c) an “intermediate” reduction of 10%. Results The results show that a total of 27,700 CVD deaths could be prevented or postponed for scenario A, 13,900 deaths for scenario B, and 5,800 for scenario C. For all scenarios, the highest percentages of DPP by type of CVD are related to ischemic heart disease, hypertensive disease, and stroke. Conclusions The results show that if Mexico considers implementing policies with greater impact to reduce sodium/salt consumption, a significant number of deaths from CVD could be prevented or postponed. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-023-15827-0.

The dietary intakes of salt from the Sodium intake was based on a previous study that used information from the National Health and Nutrition Survey 2016 (ENSANUT 2016). This study estimated mean sodium intake by population groups from 24-hour food recall questionnaires using the Mexican Food Database. Information on CVD related deaths was obtained from the General Management of Health Information (DGIS, by its Spanish acronym) of the Mexican Ministry of Health for the year 2019. Mortality data were based in the WHO International Classification of Diseases (ICD). For this study, we considered the following CVDs: ischemic heart disease, stroke, hypertensive disease, heart failure, aortic aneurysm, pulmonary embolism, and rheumatic heart disease. All the data inputs for the model were stratified by sex and age in 5-year intervals starting at 20 years of age (S1 Table1). We assumed that the standard deviation (SD) of salt intake in the counterfactual scenario was proportional to the SD at the baseline.
For estimating the sodium intake in each counterfactual scenario, we assumed that the new standard deviations remained at the same proportion to the mean consumption as that observed at the baseline. Additionally, for estimating the mean intake and the standard deviation for the WHO recommendations (sodium consumption of less than 2g/day), we considered that, in a normal distribution of sodium intake in the population, so that over 97.5% of the population would consume than 2g of sodium per day by applying the formula: Mean intake ± 2 x SD < 2 g.

Parameterization of the association between dietary consumptions and chronic diseases
The PRIME model estimates death rates associated to chronic diseases in a given situation using relative risks of different levels of nutrient intakes on chronic diseases and the baseline distribution. In the case of salt intake, the model has a two-step approach: first, it simulates the impact of changes in salt intake on blood pressure, and then it simulates the impact of the changes in blood pressure on the number of deaths averted or delayed from cardiovascular diseases. The model uses a lognormal distribution of salt intake in the population, for both the baseline and counterfactual scenarios, for sex and 5-year age groups, using the mean and standard deviation of salt intake and the population data. The net impact of the changes in the risk factor is represented by the difference of the averted deaths number between the baseline and counterfactual scenarios.

Uncertainty analyses
Considering the uncertainty of outcomes in the model, performing a probabilistic sensitivity analysis is recommended in order to explore the potential effects of reducing salt consumption on the risk factors for CVDs. In this paper, simulations were performed using the Monte Carlo methodology, which allows a stochastic (random) variation of parameters based on the sizes of the effects obtained from the literature. By using this technique, the model results were recalculated iteratively and uncertainty intervals of 95% (UI 95%) were generated for the median using the bootstrap percentile method. The model simulation was implemented the Monte Carlo analysis embedded in the PRIME and running 10,000 iterations (draws) from specified probabilistic distributions for the model input variables (salt intake, deaths and relative risks).
The macrosimulation models (both PRIME and cost evaluation) implement a Monte Carlo approach to estimate uncertainty intervals (UI) for each scenario. Each simulation runs 10,000 times. For each iteration, log-normal distributions of salt consumption, together with the relative risks from literature, are assumed for the input parameters.
The macrosimulation framework does not allow stochastic uncertainty, such as microsimulations (patient-level models), nevertheless parameter uncertainty and individual heterogeneity in our study are reflected in the reported UI. Modeling patient heterogeneity allows analyses based on individual patient characteristics that can influence the outcomes of a decision model. In this study, we have modelled discrete subgroups to represent patient heterogeneity, considering gender or age ranges, within which all individuals are assumed identical. The subgroup-specific characteristics result in subgroup-specific expected outcomes and the discrete distribution of the expected outcome across all subgroups reflects the patient heterogeneity. In addition, parameter uncertainty (2nd order uncertainty) expresses the results from lack of perfect knowledge on their true values, so parameters as relative risks are typically represented by a probability distribution that can be propagated through the model using Monte Carlo simulation, resulting in a distribution of the expected outcome, reflecting lack of perfect knowledge. Therefore, we allow the risk for CHD to be conditional on individual characteristics (i.e. age, sex, exposure to risk factorssodium intake) and consider the estimate the uncertainty of the relative risks due to sampling errors through the Monte Carlo analyses.
The structure of the models is grounded on fundamental epidemiological ideas and well-established causal pathways; therefore, we considered this type of uncertainty relatively small and did not study it.
Parameter estimation and uncertainty follow the Modeling Good Research Practices, by incorporating different concepts related to uncertainty, including the stochastic (first-order) uncertainty, the parameter (second-order) uncertainty, the structural uncertainty and the heterogeneity.
The framework of these macrosimulations allows stochastic uncertainty, parameter uncertainty, and individual heterogeneity to be reflected in the reported UI. In this kind of modelling, the heterogeneity encompasses the variability between patients that can be attributed to their characteristics, which in regression terms, would correspond to beta coefficients or the extent to which dependent variable varies by patient characteristics. In the case of these models, which are based on the sodiumhypertension-cardiovascular outcome rationale, the four sources of uncertainty are incorporated in the model, through Monte Carlo analysis, parameter parametrization and assumptions of the decision model (as the log-linear regression for the distribution of salt consumption). As macrosimulations, the heterogeneity is not considered at the individual level (as for microsimulations) but is assessed though the different exposures (sodium intake) and the parametrized relative risks that are specific to each exposure level, age-group and sex, in order to allow the reproducibility of the results.
The uncertainties, therefor, were incorporated in the final UI by implementing a 2nd order Monte Carlo analysis to estimate uncertainty in each scenario. The 2nd order Monte Carlo analysis uses two loops of iterations: the inner loop represents the variability (as the SD of the exposures) and the outer loop represents parameter uncertainty (as the RRs used in the parametrization). This also allows the model to incorporate the usual random error (sampling error) in the RR and exposure prevalence as well as other potential sources of uncertainty such as uncontrolled confounding or extrapolation from a source to a target population, because of the assumption of the portability of the RRs from the metanalyses. In case of the modeling uses in this study, the final potential impact fractions (PIF) are based on the weighted sum of the PIF for each exposure, sex and age-group strata. Again, after repeated draws and repeated calculations of the PIF, Monte Carlo limits can be obtained. Patient heterogeneity is represented by frequency distributions and analyzed with Monte Carlo simulation. Parameter uncertainty is represented by probability distributions and analyzed with 2nd-order Monte Carlo simulation (aka probabilistic sensitivity analysis).